Project summary The Human Genome Project and subsequent projects such as 1000 Genomes, GTEx, and ENCODE provide powerful resources for the identification of genes that predispose to human diseases and determine variability in disease-related quantitative traits. Along with these resources have come increasingly efficient tools to genotype, sequence, and annotate the human genome, and to support computation across these data. These resources and tools will be critical as we continue to unravel the complex etiologic basis of common human diseases and disease-related quantitative traits. In this proposal, we describe a set of statistical and computational problems that arise in human /gene mapping, with a particular emphasis on genotype-array and sequence-based genome-wide association studies (GWAS). We describe how we will solve these problems through analytic methods, computer simulation, and testing on a diverse set of complex trait genetics data, and how we will generalize these solutions through the production, distribution, and support of efficient computer software. Specifically, we will: (a) more accurately estimate genotype imputation quality for low-frequency (0.5%<minor allele frequency [MAF]<5%) and rare (MAF<0.5%) genetic variants and so increase power of tests of association; (b) determine efficient designs for low-frequency and rare-variant GWAS that combine sequencing, genotyping, and genotype imputation; (c) build interactive, easy-to-use tools to display single-variant and multiple-variant (aggregation) genetic association results and relevant annotations; and (d) continue to update, distribute, and support software we will and have developed in this project , including tools to detect contamination in RNA-Seq and related studies . We will also continue to be opportunistic in identifying and addressing important design and analysis problems that relate to the goals of this project. Under separate funding, we will apply the resulting methods and tools to help understand the genetic basis of type 2 diabetes and related quantitative traits, schizophrenia, and bipolar disorder. Success in these aims will result in more powerful human genetic studies and enable more rapid identification of variants that predispose to human disease and account for variability in disease-related quantitative traits. Identification of these causal variants has the potential to lead to new insights into basic biology and disease etiology, identify novel therapies, improve targeting of therapies, assist in disease classification, and support more accurate prediction of disease risk.